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Actividad
Congreso Digital Humanities 2018 DH2018
A machine learning methodology to analyse 3D digital models of cultural heritage objects
Ponente. Diego Jiménez Badillo
Thanks to recent advances in scanning technologies there has been an increase in the number of methods developed for digitizing cultural heritage objects. Many of the resulting 3D models are used for visualization or archiving purposes. Unfortunately, there are still few projects oriented to gain archaeological knowledge from point clouds and triangular meshes.
In this paper we present some results of an ongoing project that applies machine learning and computer vision techniques for recognizing, retrieving and classifying cultural heritage objects in an automatic way. The presentation focuses specifically on a method to analyze style variations of archaeological artefacts with minimal human intervention. This is based on a 3D morphing algorithm proposed by Shelton (2000). Our implementation allows analyzing pairs of objects whose shapes represent the canonical extremes of a continuum, that is, objects that belong to two different “styles” within a cultural tradition. The purpose of the algorithm is taking two extreme shapes (i.e. 3D point-clouds, surface meshes or 3D digital models) as input in order to extract several 3D virtual models whose shape or “style” lies “in-between” the two extremes. This is useful in situations where archaeologists need to decide to which extreme a real artefact is more similar.
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